





"""simple docstring"""





from __future__ import annotations

from typing import Any

class 						lowercase   :





      def __init__(			self ,		lowercase					)						->	None:
            lowerCAmelCase    		=			num_of_nodes
            lowerCAmelCase    		=			[]
            lowerCAmelCase    		=			{}





      def        _snake_case     (			self ,		lowercase ,		lowercase ,		lowercase					)						->	None:
            self.m_edges.append([u_node, v_node, weight]					)





      def        _snake_case     (			self ,		lowercase					)						->	int:

            if self.m_component[u_node] == u_node:
                  return u_node
            return self.find_component(self.m_component[u_node]					)





      def        _snake_case     (			self ,		lowercase					)						->	None:

            if self.m_component[u_node] != u_node:
                  for k in self.m_component:
                        lowerCAmelCase    		=			self.find_component(lowercase					)





      def        _snake_case     (			self ,		lowercase ,		lowercase ,		lowercase					)						->	None:

            if component_size[u_node] <= component_size[v_node]:
                  lowerCAmelCase    		=			v_node
                  component_size[v_node] += component_size[u_node]
                  self.set_component(lowercase					)

            elif component_size[u_node] >= component_size[v_node]:
                  lowerCAmelCase    		=			self.find_component(lowercase					)
                  component_size[u_node] += component_size[v_node]
                  self.set_component(lowercase					)





      def        _snake_case     (			self					)						->	None:
            lowerCAmelCase    		=			[]
            lowerCAmelCase    		=			0

            lowerCAmelCase    		=			[-1] * self.m_num_of_nodes

            # A list of components (initialized to all of the nodes)
            for node in range(self.m_num_of_nodes					):
                  self.m_component.update({node: node}					)
                  component_size.append(1					)

            lowerCAmelCase    		=			self.m_num_of_nodes

            while num_of_components > 1:
                  for edge in self.m_edges:
                        lowerCAmelCase		,						lowerCAmelCase		,						lowerCAmelCase    		=			edge

                        lowerCAmelCase    		=			self.m_component[u]
                        lowerCAmelCase    		=			self.m_component[v]

                        if u_component != v_component:

                              for component in (u_component, v_component):
                                    if (
                                        minimum_weight_edge[component] == -1
                                        or minimum_weight_edge[component][2] > w
                                    ):
                                          lowerCAmelCase    		=			[u, v, w]

                  for edge in minimum_weight_edge:
                        if isinstance(lowercase ,		lowercase					):
                              lowerCAmelCase		,						lowerCAmelCase		,						lowerCAmelCase    		=			edge

                              lowerCAmelCase    		=			self.m_component[u]
                              lowerCAmelCase    		=			self.m_component[v]

                              if u_component != v_component:
                                    mst_weight += w
                                    self.union(lowercase ,		lowercase ,		lowercase					)
                                    print(f'Added edge [{u} - {v}]\nAdded weight: {w}\n'					)
                                    num_of_components -= 1

                  lowerCAmelCase    		=			[-1] * self.m_num_of_nodes
            print(f'The total weight of the minimal spanning tree is: {mst_weight}'					)







def      UpperCAmelCase__				(    ):







      '''simple docstring'''






if __name__ == "__main__":
       import doctest

       doctest.testmod()













"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE__   =  {
    "configuration_roformer": ["ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoFormerConfig", "RoFormerOnnxConfig"],
    "tokenization_roformer": ["RoFormerTokenizer"],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       SCREAMING_SNAKE_CASE__   =  ["RoFormerTokenizerFast"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       SCREAMING_SNAKE_CASE__   =  [
           "ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
           "RoFormerForCausalLM",
           "RoFormerForMaskedLM",
           "RoFormerForMultipleChoice",
           "RoFormerForQuestionAnswering",
           "RoFormerForSequenceClassification",
           "RoFormerForTokenClassification",
           "RoFormerLayer",
           "RoFormerModel",
           "RoFormerPreTrainedModel",
           "load_tf_weights_in_roformer",
       ]


try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       SCREAMING_SNAKE_CASE__   =  [
           "TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TFRoFormerForCausalLM",
           "TFRoFormerForMaskedLM",
           "TFRoFormerForMultipleChoice",
           "TFRoFormerForQuestionAnswering",
           "TFRoFormerForSequenceClassification",
           "TFRoFormerForTokenClassification",
           "TFRoFormerLayer",
           "TFRoFormerModel",
           "TFRoFormerPreTrainedModel",
       ]


try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       SCREAMING_SNAKE_CASE__   =  [
           "FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
           "FlaxRoFormerForMaskedLM",
           "FlaxRoFormerForMultipleChoice",
           "FlaxRoFormerForQuestionAnswering",
           "FlaxRoFormerForSequenceClassification",
           "FlaxRoFormerForTokenClassification",
           "FlaxRoFormerModel",
           "FlaxRoFormerPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
       from .tokenization_roformer import RoFormerTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_roformer_fast import RoFormerTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_roformer import (
                  ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  RoFormerForCausalLM,
                  RoFormerForMaskedLM,
                  RoFormerForMultipleChoice,
                  RoFormerForQuestionAnswering,
                  RoFormerForSequenceClassification,
                  RoFormerForTokenClassification,
                  RoFormerLayer,
                  RoFormerModel,
                  RoFormerPreTrainedModel,
                  load_tf_weights_in_roformer,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_roformer import (
                  TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFRoFormerForCausalLM,
                  TFRoFormerForMaskedLM,
                  TFRoFormerForMultipleChoice,
                  TFRoFormerForQuestionAnswering,
                  TFRoFormerForSequenceClassification,
                  TFRoFormerForTokenClassification,
                  TFRoFormerLayer,
                  TFRoFormerModel,
                  TFRoFormerPreTrainedModel,
              )

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_roformer import (
                  FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  FlaxRoFormerForMaskedLM,
                  FlaxRoFormerForMultipleChoice,
                  FlaxRoFormerForQuestionAnswering,
                  FlaxRoFormerForSequenceClassification,
                  FlaxRoFormerForTokenClassification,
                  FlaxRoFormerModel,
                  FlaxRoFormerPreTrainedModel,
              )


else:
       import sys

       SCREAMING_SNAKE_CASE__   =  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






